Applications of Mitscherlich Baule function: a robust regression approach
Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which help policymakers formulating public policies. Therefore, Mitscherlich Baule function was implemented using robust regression model based on a growth equation. In Mitscherlich Baule Function two methods...
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| Language: | English |
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Taylor & Francis
2024-12-01
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| Series: | Research in Statistics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27684520.2024.2321621 |
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| author | Rizwan Yousuf Manish Sharma |
| author_facet | Rizwan Yousuf Manish Sharma |
| author_sort | Rizwan Yousuf |
| collection | DOAJ |
| description | Consistent estimation techniques need to be implemented to obtain robust empirical outcomes which help policymakers formulating public policies. Therefore, Mitscherlich Baule function was implemented using robust regression model based on a growth equation. In Mitscherlich Baule Function two methods were used one least square Method and Iteratively Reweighted Least Square Method. In Least Square method Gauss Newton Method, was used for the study, whereas in Iteratively Reweighted Least Square method Marquardt Method and Gradient Method was used. Secondary data has been used in the study. Classical and robust procedures were employed for the estimation of the parameters. Thus empirical results reveal that the overall fit of the model improves in case of Marquardt technique. Thus, empirical findings exhibit that the results, obtained through Marquardt method are better than LS techniques. The present study focus on when you are dealing with nonlinear production function Mitscherlich Baule function comes out to be best for handling outliers in data sets and also helpful for policy makers for formulating public policies. |
| format | Article |
| id | doaj-art-17fe126ce2134d6baf74a2bb25fcee81 |
| institution | DOAJ |
| issn | 2768-4520 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis |
| record_format | Article |
| series | Research in Statistics |
| spelling | doaj-art-17fe126ce2134d6baf74a2bb25fcee812025-08-20T02:50:56ZengTaylor & FrancisResearch in Statistics2768-45202024-12-012110.1080/27684520.2024.2321621Applications of Mitscherlich Baule function: a robust regression approachRizwan Yousuf0Manish Sharma1Department of Mathematics, Chandigarh University, Punjab, IndiaDivision of Statistics and Computer Science, Skuast Jammu, Jammu, IndiaConsistent estimation techniques need to be implemented to obtain robust empirical outcomes which help policymakers formulating public policies. Therefore, Mitscherlich Baule function was implemented using robust regression model based on a growth equation. In Mitscherlich Baule Function two methods were used one least square Method and Iteratively Reweighted Least Square Method. In Least Square method Gauss Newton Method, was used for the study, whereas in Iteratively Reweighted Least Square method Marquardt Method and Gradient Method was used. Secondary data has been used in the study. Classical and robust procedures were employed for the estimation of the parameters. Thus empirical results reveal that the overall fit of the model improves in case of Marquardt technique. Thus, empirical findings exhibit that the results, obtained through Marquardt method are better than LS techniques. The present study focus on when you are dealing with nonlinear production function Mitscherlich Baule function comes out to be best for handling outliers in data sets and also helpful for policy makers for formulating public policies.https://www.tandfonline.com/doi/10.1080/27684520.2024.2321621Mitscherlich Baule FunctionMarquardt Methodgradient methoditeratively reweighted least square |
| spellingShingle | Rizwan Yousuf Manish Sharma Applications of Mitscherlich Baule function: a robust regression approach Research in Statistics Mitscherlich Baule Function Marquardt Method gradient method iteratively reweighted least square |
| title | Applications of Mitscherlich Baule function: a robust regression approach |
| title_full | Applications of Mitscherlich Baule function: a robust regression approach |
| title_fullStr | Applications of Mitscherlich Baule function: a robust regression approach |
| title_full_unstemmed | Applications of Mitscherlich Baule function: a robust regression approach |
| title_short | Applications of Mitscherlich Baule function: a robust regression approach |
| title_sort | applications of mitscherlich baule function a robust regression approach |
| topic | Mitscherlich Baule Function Marquardt Method gradient method iteratively reweighted least square |
| url | https://www.tandfonline.com/doi/10.1080/27684520.2024.2321621 |
| work_keys_str_mv | AT rizwanyousuf applicationsofmitscherlichbaulefunctionarobustregressionapproach AT manishsharma applicationsofmitscherlichbaulefunctionarobustregressionapproach |